The hottest Neural Networks Substack posts right now

And their main takeaways
Category
Top Technology Topics

Grounding Large Language Models in a Cognitive Foundation

The Gradient • 20 implied HN points • 15 Apr 23
🕹 Technology AI Robotics Neural Networks Speech Recognition
  1. Intelligent robots have struggled commercially due to the challenge of having meaningful conversations with them.
  2. Recent advancements in AI, speech recognition, and large language models like ChatGPT and GPT-4 have opened up new possibilities.
  3. For robots to effectively interact in the physical world, they need to quickly adapt to context and be localized in their knowledge.

Becoming One with the Machine

Malt Liquidity • 6 implied HN points • 13 Mar 24
🕹 Technology AI Machine Learning Self-driving cars Computer Vision Neural Networks
  1. Our brain is exceptional at pattern recognition, and merging with technology can enhance our abilities.
  2. Visual processing is faster than auditory processing, like in chess where seeing the board is more efficient than listening to a game.
  3. Technology, like AI, can help turbocharge our skills by providing new perspectives and automating processes, leading to more creative problem-solving.

The Past and Present of Computer-Augmented Hypothesis Generation

FreakTakes • 11 implied HN points • 10 Aug 23
🔬 Science Chemistry Machine Learning Neural Networks
  1. Computer-augmented hypothesis generation is a promising concept that can help uncover new and valuable ideas from existing data.
  2. Looking at old research in a new light can lead to significant breakthroughs, as seen with Don Swanson's and Sharpless' work in different fields.
  3. Tools like LLMs can assist researchers in finding connections between disparate data points, potentially unlocking new avenues for scientific discovery.

Yes, AIs ‘understand’ things

Nonzero Newsletter • 5 HN points • 22 Feb 24
🕹 Technology AI Philosophy Information processing Neural Networks Understanding
  1. The classic argument against AI understanding, the Chinese Room thought experiment, is challenged by large language models.
  2. Large language models (LLMs) like ChatGPT demonstrate elements of understanding by processing information similarly to human brains when it comes to understanding.
  3. LLMs show semantic understanding by mapping words to meaning, undermining the belief that AIs have no semantics and only syntax as argued by Searle in the Chinese Room thought experiment.
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LLMs, anthropocentric thinking, accuracy, and self-driving

Apperceptive (moved to buttondown) • 16 implied HN points • 16 Feb 23
🕹 Technology AI Machine Learning Neural Networks UX
  1. Large language models are different from earlier neural network models in architecture and scale of training data.
  2. Large language models exploit the anthropomorphic fallacy, making people interpret them as conscious beings.
  3. The illusion of cognitive depth in machine learning systems like large language models can lead to misunderstandings and challenges in applications like autonomous cars.

Update #48: Generative AI in Law & Art and Promptable Vision Models

The Gradient • 11 implied HN points • 25 Apr 23
🕹 Technology AI Generative models Neural Networks Research Ethical Implications
  1. Generative AI is transforming fields like Law and Art, raising ethical and legal questions about ownership and bias.
  2. Recent models allow users to specify vision tasks through flexible prompts, enabling diverse applications in image segmentation and visual tasks.
  3. Advances in promptable vision models and generative AI pose challenges and opportunities, from disrupting professions to potential ethical and legal implications.

Update #43: Propaganda Deepfakes and Transformers get Loopy

The Gradient • 11 implied HN points • 14 Feb 23
🕹 Technology AI Deepfakes Neural Networks Language Models Research
  1. Deepfakes were used for spreading state-aligned propaganda for the first time, raising concerns about the spread of misinformation.
  2. Transformers embedded in loops can function like Turing complete computers, showing their expressive power and potential for programming.
  3. As generative models evolve, it becomes crucial to anticipate and address the potential misuse of technology for harmful or misleading content.

Data Science Weekly - Issue 418

Data Science Weekly Newsletter • 19 implied HN points • 25 Nov 21
🕹 Technology Data science Machine Learning Artificial Intelligence Neural Networks Natural Language
  1. Understanding data strategy is crucial for companies. Many invest in data, but few create a data-driven culture.
  2. Deep learning can help with smart, autonomous systems, but caution is needed in safety-critical applications.
  3. Tools like Retool make it easier for teams to build applications on their data without needing extensive coding skills.

Why Do A.I. Image Generators Have Problems Creating Hands?

I'll Keep This Short • 5 implied HN points • 14 Aug 23
🕹 Technology AI Neural Networks Image Generation Machine Learning Interpretability
  1. A.I. image generators struggle with creating hands due to the complexity of hand shapes and poses
  2. Neural networks power image generators through mathematical transforms
  3. Efforts are being made to improve A.I. image generation by addressing challenges like hand creation through interpretability of neural networks

Deep Learning Is Better Than Linear Regression

As Clay Awakens • 2 HN points • 19 Mar 23
🕹 Technology Deep Learning Machine Learning Data science Neural Networks
  1. Linear regression is a reliable, stable, and simple technique with a long history of successful applications.
  2. Deep learning, especially non-linear regression, has shown significant advancements over the past decade and can outperform linear regression in many real-world tasks.
  3. Deep learning models have the ability to automatically learn and discover complex features, making them advantageous over manually engineered features in linear regression.

ChatGPT Learns Fintech

Chaos Engineering • 5 implied HN points • 24 Feb 23
🕹 Technology AI Fintech Machine Learning Neural Networks Data Modeling
  1. ChatGPT can learn some superficial aspects of finance but needs explicit training to become a financial expert.
  2. For ChatGPT to learn fintech, a hybrid architecture combining its pretrained model with a specific ML model optimized for financial tasks is necessary.
  3. Improving ChatGPT's understanding of finance requires training it on structured financial data and updating its architecture to process dense, numeric data.

Data Science Weekly - Issue 346

Data Science Weekly Newsletter • 19 implied HN points • 09 Jul 20
🕹 Technology Artificial Intelligence Data science Machine Learning Neural Networks Computing
  1. AI training costs are dropping much faster than usual, which means AI technology is becoming easier and cheaper to develop. This could lead to more companies using AI over the next decade.
  2. Training Generative Adversarial Networks (GANs) can be tough, but there are new algorithms that help make it more stable and efficient. This is important for many applications in science and engineering.
  3. Moving from traditional statistics to machine learning involves a different way of thinking. Understanding this shift can help those with a stats background adapt and excel in machine learning.

Why We Won't Achieve AGI Until Memory Is A Core Architectural Component

Am I Stronger Yet? • 3 HN points • 09 Aug 23
🕹 Technology AI Memory Neural Networks
  1. Memory is central to almost everything we do, and different types of memory are crucial for complex tasks.
  2. Current mechanisms for equipping LLMs with memory have limitations, such as static model weights and limited token buffers.
  3. To achieve human-level intelligence, a breakthrough in long-term memory integration is necessary for AIs to undertake deep work.

Data Science Weekly - Issue 315

Data Science Weekly Newsletter • 19 implied HN points • 05 Dec 19
🕹 Technology AI Machine Learning Data science Data Visualization Neural Networks
  1. New technology is helping scientists study animals more effectively, but it's also creating a lot of data to handle.
  2. Machine learning tools are still complex and unique, making it tough for researchers to replicate their work easily.
  3. Recent advancements in machine learning are uncovering historical authorship details, like who wrote parts of Shakespeare's plays.

DiffGrad : Is it the right optimization method for training your CNNs?

Machine Learning Diaries • 2 HN points • 25 Sep 23
🔬 Science Optimization Machine Learning Neural Networks Research
  1. Optimizing neural networks with DiffGrad may prevent slow learning and jittering effects in training
  2. DiffGrad adjusts learning rates based on gradient behavior for each parameter, leading to improved optimization
  3. Comparisons suggest that DiffGrad outperformed Adam optimizer in terms of avoiding overshooting global minima

Neural Network Diffusion

Gonzo ML • 1 HN point • 26 Feb 24
🕹 Technology Neural Networks Model Evaluation
  1. Hypernetworks involve one neural network generating weights for another - still a relatively unknown but promising concept worth exploring further.
  2. Diffusion models involve adding noise (forward) and removing noise (reverse) gradually to reveal hidden details - a strategy utilized effectively in the study.
  3. Neural Network Diffusion (p-diff) involves training an autoencoder on neural network parameters to convert and regenerate weights, showing promising results across various datasets and network architectures.

Data Science Weekly - Issue 290

Data Science Weekly Newsletter • 19 implied HN points • 13 Jun 19
🕹 Technology AI Data science Machine Learning Neural Networks
  1. Facebook has created an AI that can mimic voices, even famous ones like Bill Gates. This technology raises questions about voice authenticity and security.
  2. Machine learning is enabling parents to potentially select traits like intelligence for their children through genetic choices. This could change how we think about heredity.
  3. Deepfake technology is becoming increasingly accessible, allowing users to easily edit videos and create convincing fake content. This poses a challenge for misinformation and trust in media.

Data Science Weekly - Issue 283

Data Science Weekly Newsletter • 19 implied HN points • 25 Apr 19
🕹 Technology Artificial Intelligence Data science Machine Learning Neural Networks Surveillance technology
  1. Training neural networks can be tricky, and it's important to understand common mistakes to get good results.
  2. AI is making big waves in various fields, including music and scientific research, showing how versatile it can be.
  3. Data scientists need to know the business and the data well, or they risk becoming bottlenecked and less effective.

Data Science Weekly - Issue 274

Data Science Weekly Newsletter • 19 implied HN points • 21 Feb 19
🕹 Technology Data science Machine Learning Artificial Intelligence Predictive Modeling Neural Networks
  1. The visual search engine project for Hayneedle shows how search can be enhanced by using images instead of words. This could make finding products easier for customers.
  2. Mathematicians are starting to understand how the design of neural networks affects their capabilities. This can help in optimizing their use for various tasks.
  3. Knowing your data thoroughly is crucial for anyone working in data science. It's essential to understand where the data comes from and what it represents.

Data Science Weekly - Issue 235

Data Science Weekly Newsletter • 19 implied HN points • 24 May 18
🕹 Technology Data science Machine Learning Artificial Intelligence Neural Networks Statistical Analysis
  1. Deep learning models are making it easier to categorize images, like those used in Airbnb listings.
  2. New research suggests that the brain may store information in a discrete way, which could change our understanding of brain and technology interactions.
  3. There are many resources available for learning data science, including online programs and tutorials that cover various tools and techniques.

Data Science Weekly - Issue 222

Data Science Weekly Newsletter • 19 implied HN points • 22 Feb 18
🕹 Technology Data science Artificial Intelligence Machine Learning Neural Networks Deep Learning
  1. A moth's brain can learn to recognize odors faster than AI can, showing a fascinating aspect of how natural intelligence works.
  2. There's a shortage of AI talent, with only around 22,000 people worldwide having the necessary skills, which is a big challenge for the industry.
  3. New AI technologies are learning to be creative by understanding rules and then finding ways to break them, which could lead to innovative solutions.

What is an embedding, anyways?

Simplicity is SOTA • 2 HN points • 27 Mar 23
🕹 Technology Machine Learning Neural Networks Embeddings Data Transformation
  1. The concept of 'embedding' in machine learning has evolved and become widely used, replacing terms like vectors and representations.
  2. Embeddings can be applied to various types of data, come from different layers in a neural network, and are not always about reducing dimensions.
  3. Defining 'embedding' has become challenging due to its widespread use, but the essence is about learned transformations that make data more useful.

Data Science Weekly - Issue 204

Data Science Weekly Newsletter • 19 implied HN points • 19 Oct 17
🕹 Technology AI Machine Learning Data science Deep Learning Neural Networks
  1. Google is working on smart software that can create other software, making tech easier and more efficient.
  2. Our brains limit us to having meaningful relationships with only about five close friends, which is interesting for understanding social networks.
  3. There are many resources available, like open-source tools and training, that can help anyone learn data science and AI skills easily.

Why are neural networks so powerful?

The Palindrome • 1 implied HN point • 11 Sep 23
🔬 Science Machine Learning Neural Networks Mathematics
  1. Neural networks are powerful due to their ability to closely approximate almost any function.
  2. Machine learning involves finding a function that approximates the relationship between data points and their ground truth.
  3. Approximation theory seeks to find a simple function close enough to a complex one by determining the right function family and precise approximation within that family.

Data Science Weekly - Issue 128

Data Science Weekly Newsletter • 19 implied HN points • 05 May 16
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Neural Networks
  1. Kaggle competitions need more than just machine learning knowledge. It's important to have the right mindset and explore the data thoroughly.
  2. Neural networks are surprisingly good at compressing data. They can learn to behave effectively without being explicitly taught how.
  3. Machine learning can unintentionally reinforce social biases. It's crucial to recognize these biases and work to reduce their impact in models.

Data Science Weekly - Issue 112

Data Science Weekly Newsletter • 19 implied HN points • 14 Jan 16
🕹 Technology Data science AI Machine Learning Neural Networks Deep Learning Statistical Analysis
  1. The value of information is important in decision-making. Knowing how much to pay for good information can help you make better choices.
  2. AI is getting better at understanding humor. It was thought machines couldn't grasp humor, but advancements are changing that view.
  3. Participating in hackathons can fast-track your learning. Working with others on projects can teach you more than studying alone for months.

Data Science Weekly - Issue 94

Data Science Weekly Newsletter • 19 implied HN points • 10 Sep 15
🕹 Technology Data science Machine Learning Neural Networks Statistics API Development
  1. Data science combines skills from statistics and computer science to analyze and interpret complex data. It's a growing field that's seen as crucial for modern businesses.
  2. Neural networks are important in deep learning, allowing computers to identify patterns and make predictions. They can be complex but are essential for many applications like image and speech recognition.
  3. Understanding foundational topics, like probability and linear algebra, is key for anyone wanting to succeed in data science. There are plenty of resources available to help learn these subjects.